Feature Engineering for Modern Machine Learning with Scikit-Learn

5.2 Dealing with Imbalanced Data: SMOTE, Class Weighting

Imbalanced data presents a significant challenge in machine learning, particularly in classification tasks where one class substantially outnumbers others. This imbalance can lead to models developing a strong bias toward the majority class, resulting in poor performance when predicting the minority class. To address this issue, data scientists employ various techniques to create a more balanced representation of classes during model training.

Two prominent methods for handling imbalanced datasets are the Synthetic Minority Oversampling Technique (SMOTE) and Class Weighting. SMOTE works by generating synthetic samples for the minority class, effectively increasing its representation in the ...